Overview

Dataset statistics

Number of variables19
Number of observations174389
Missing cells0
Missing cells (%)0.0%
Duplicate rows2159
Duplicate rows (%)1.2%
Total size in memory71.9 MiB
Average record size in memory432.1 B

Variable types

Numeric13
Categorical6

Warnings

Dataset has 2159 (1.2%) duplicate rows Duplicates
artists has a high cardinality: 36195 distinct values High cardinality
id has a high cardinality: 172230 distinct values High cardinality
name has a high cardinality: 137013 distinct values High cardinality
release_date has a high cardinality: 11043 distinct values High cardinality
id is uniformly distributed Uniform
instrumentalness has 43652 (25.0%) zeros Zeros
key has 21967 (12.6%) zeros Zeros
popularity has 40905 (23.5%) zeros Zeros

Reproduction

Analysis started2022-09-30 19:40:20.721198
Analysis finished2022-09-30 19:41:05.997215
Duration45.28 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

acousticness
Real number (ℝ≥0)

Distinct4929
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4992284818
Minimum0
Maximum0.996
Zeros19
Zeros (%)< 0.1%
Memory size1.3 MiB
2022-09-30T21:41:06.119214image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.000717
Q10.0877
median0.517
Q30.895
95-th percentile0.992
Maximum0.996
Range0.996
Interquartile range (IQR)0.8073

Descriptive statistics

Standard deviation0.3799358419
Coefficient of variation (CV)0.7610460055
Kurtosis-1.620262282
Mean0.4992284818
Median Absolute Deviation (MAD)0.401
Skewness-0.03451775614
Sum87059.95571
Variance0.1443512439
MonotocityNot monotonic
2022-09-30T21:41:06.234886image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9953150
 
1.8%
0.9942363
 
1.4%
0.9931833
 
1.1%
0.9921543
 
0.9%
0.9911354
 
0.8%
0.991201
 
0.7%
0.9891106
 
0.6%
0.9961090
 
0.6%
0.988973
 
0.6%
0.987866
 
0.5%
Other values (4919)158910
91.1%
ValueCountFrequency (%)
019
< 0.1%
1 × 1061
 
< 0.1%
1.01 × 1062
 
< 0.1%
1.02 × 1061
 
< 0.1%
1.03 × 1061
 
< 0.1%
1.04 × 1062
 
< 0.1%
1.05 × 1061
 
< 0.1%
1.07 × 1061
 
< 0.1%
1.08 × 1061
 
< 0.1%
1.09 × 1061
 
< 0.1%
ValueCountFrequency (%)
0.9961090
 
0.6%
0.9953150
1.8%
0.9942363
1.4%
0.9931833
1.1%
0.9921543
0.9%
0.9911354
0.8%
0.991201
 
0.7%
0.9891106
 
0.6%
0.988973
 
0.6%
0.987866
 
0.5%

artists
Categorical

HIGH CARDINALITY

Distinct36195
Distinct (%)20.8%
Missing0
Missing (%)0.0%
Memory size13.8 MiB
['Tadeusz Dolega Mostowicz']
 
1281
['Эрнест Хемингуэй']
 
1175
['Эрих Мария Ремарк']
 
1062
['Francisco Canaro']
 
951
['Ignacio Corsini']
 
624
Other values (36190)
169296 

Length

Max length498
Median length18
Mean length23.85919983
Min length5

Characters and Unicode

Total characters4160782
Distinct characters656
Distinct categories19 ?
Distinct scripts11 ?
Distinct blocks12 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21395 ?
Unique (%)12.3%

Sample

1st row['Mamie Smith']
2nd row["Screamin' Jay Hawkins"]
3rd row['Mamie Smith']
4th row['Oscar Velazquez']
5th row['Mixe']
ValueCountFrequency (%)
['Tadeusz Dolega Mostowicz']1281
 
0.7%
['Эрнест Хемингуэй']1175
 
0.7%
['Эрих Мария Ремарк']1062
 
0.6%
['Francisco Canaro']951
 
0.5%
['Ignacio Corsini']624
 
0.4%
['Frank Sinatra']621
 
0.4%
['Elvis Presley']494
 
0.3%
['Bob Dylan']459
 
0.3%
['Francisco Canaro', 'Charlo']456
 
0.3%
['Johnny Cash']456
 
0.3%
Other values (36185)166810
95.7%
2022-09-30T21:41:06.553151image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the17693
 
3.4%
6384
 
1.2%
orchestra6200
 
1.2%
john2532
 
0.5%
francisco2360
 
0.5%
canaro2254
 
0.4%
van2037
 
0.4%
his1815
 
0.4%
de1793
 
0.3%
of1784
 
0.3%
Other values (29061)468380
91.3%

Most occurring characters

ValueCountFrequency (%)
'466586
 
11.2%
338844
 
8.1%
e275977
 
6.6%
a263891
 
6.3%
r203066
 
4.9%
n197300
 
4.7%
i194008
 
4.7%
o190382
 
4.6%
[174395
 
4.2%
]174395
 
4.2%
Other values (646)1681938
40.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2384834
57.3%
Other Punctuation549399
 
13.2%
Uppercase Letter525257
 
12.6%
Space Separator338844
 
8.1%
Close Punctuation174578
 
4.2%
Open Punctuation174576
 
4.2%
Decimal Number6493
 
0.2%
Dash Punctuation3080
 
0.1%
Other Letter2865
 
0.1%
Nonspacing Mark381
 
< 0.1%
Other values (9)475
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
146
 
5.1%
142
 
5.0%
132
 
4.6%
115
 
4.0%
113
 
3.9%
79
 
2.8%
74
 
2.6%
71
 
2.5%
67
 
2.3%
55
 
1.9%
Other values (355)1871
65.3%
ValueCountFrequency (%)
e275977
11.6%
a263891
11.1%
r203066
 
8.5%
n197300
 
8.3%
i194008
 
8.1%
o190382
 
8.0%
l135840
 
5.7%
s135471
 
5.7%
t121490
 
5.1%
h94850
 
4.0%
Other values (132)572559
24.0%
ValueCountFrequency (%)
S43668
 
8.3%
B39666
 
7.6%
M39116
 
7.4%
T38802
 
7.4%
C38352
 
7.3%
A27756
 
5.3%
D27235
 
5.2%
R25932
 
4.9%
J25706
 
4.9%
P24031
 
4.6%
Other values (75)194993
37.1%
ValueCountFrequency (%)
'466586
84.9%
,61286
 
11.2%
.8656
 
1.6%
&5979
 
1.1%
"5419
 
1.0%
/819
 
0.1%
!408
 
0.1%
*62
 
< 0.1%
:37
 
< 0.1%
\36
 
< 0.1%
Other values (8)111
 
< 0.1%
ValueCountFrequency (%)
138
36.2%
103
27.0%
67
17.6%
16
 
4.2%
15
 
3.9%
12
 
3.1%
11
 
2.9%
7
 
1.8%
5
 
1.3%
5
 
1.3%
Other values (2)2
 
0.5%
ValueCountFrequency (%)
21225
18.9%
01151
17.7%
1998
15.4%
5611
9.4%
9555
8.5%
7478
 
7.4%
3417
 
6.4%
8388
 
6.0%
4388
 
6.0%
6282
 
4.3%
ValueCountFrequency (%)
-3038
98.6%
40
 
1.3%
2
 
0.1%
ValueCountFrequency (%)
39
53.4%
26
35.6%
»8
 
11.0%
ValueCountFrequency (%)
[174395
99.9%
(181
 
0.1%
ValueCountFrequency (%)
]174395
99.9%
)183
 
0.1%
ValueCountFrequency (%)
+45
88.2%
|6
 
11.8%
ValueCountFrequency (%)
®2
66.7%
1
33.3%
ValueCountFrequency (%)
´3
60.0%
`2
40.0%
ValueCountFrequency (%)
³1
50.0%
²1
50.0%
ValueCountFrequency (%)
«8
72.7%
3
 
27.3%
ValueCountFrequency (%)
338844
100.0%
ValueCountFrequency (%)
$297
100.0%
ValueCountFrequency (%)
_19
100.0%
ValueCountFrequency (%)
14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2864870
68.9%
Common1247445
30.0%
Cyrillic36913
 
0.9%
Greek8320
 
0.2%
Thai1869
 
< 0.1%
Han751
 
< 0.1%
Katakana289
 
< 0.1%
Hebrew204
 
< 0.1%
Arabic64
 
< 0.1%
Hiragana32
 
< 0.1%

Most frequent character per script

ValueCountFrequency (%)
35
 
4.7%
34
 
4.5%
30
 
4.0%
30
 
4.0%
29
 
3.9%
29
 
3.9%
27
 
3.6%
25
 
3.3%
24
 
3.2%
24
 
3.2%
Other values (207)464
61.8%
ValueCountFrequency (%)
e275977
 
9.6%
a263891
 
9.2%
r203066
 
7.1%
n197300
 
6.9%
i194008
 
6.8%
o190382
 
6.6%
l135840
 
4.7%
s135471
 
4.7%
t121490
 
4.2%
h94850
 
3.3%
Other values (113)1052595
36.7%
ValueCountFrequency (%)
ς755
 
9.1%
α720
 
8.7%
ο521
 
6.3%
τ496
 
6.0%
ρ481
 
5.8%
η449
 
5.4%
ν373
 
4.5%
λ310
 
3.7%
ι302
 
3.6%
κ283
 
3.4%
Other values (43)3630
43.6%
ValueCountFrequency (%)
'466586
37.4%
338844
27.2%
[174395
 
14.0%
]174395
 
14.0%
,61286
 
4.9%
.8656
 
0.7%
&5979
 
0.5%
"5419
 
0.4%
-3038
 
0.2%
21225
 
0.1%
Other values (42)7622
 
0.6%
ValueCountFrequency (%)
р4600
12.5%
е3857
 
10.4%
и3390
 
9.2%
н2623
 
7.1%
м2437
 
6.6%
а2381
 
6.5%
Э2240
 
6.1%
т1407
 
3.8%
у1359
 
3.7%
с1246
 
3.4%
Other values (42)11373
30.8%
ValueCountFrequency (%)
146
 
7.8%
142
 
7.6%
138
 
7.4%
132
 
7.1%
115
 
6.2%
113
 
6.0%
103
 
5.5%
79
 
4.2%
74
 
4.0%
71
 
3.8%
Other values (41)756
40.4%
ValueCountFrequency (%)
29
 
10.0%
16
 
5.5%
15
 
5.2%
15
 
5.2%
14
 
4.8%
13
 
4.5%
12
 
4.2%
12
 
4.2%
11
 
3.8%
11
 
3.8%
Other values (38)141
48.8%
ValueCountFrequency (%)
2
 
8.0%
2
 
8.0%
2
 
8.0%
1
 
4.0%
1
 
4.0%
1
 
4.0%
1
 
4.0%
1
 
4.0%
1
 
4.0%
1
 
4.0%
Other values (12)12
48.0%
ValueCountFrequency (%)
11
34.4%
3
 
9.4%
3
 
9.4%
2
 
6.2%
2
 
6.2%
1
 
3.1%
1
 
3.1%
1
 
3.1%
1
 
3.1%
1
 
3.1%
Other values (6)6
18.8%
ValueCountFrequency (%)
م14
21.9%
د9
14.1%
ي9
14.1%
ح7
10.9%
ف5
 
7.8%
و5
 
7.8%
ز5
 
7.8%
ا2
 
3.1%
ل2
 
3.1%
إ2
 
3.1%
Other values (2)4
 
6.2%
ValueCountFrequency (%)
ו48
23.5%
א24
11.8%
מ24
11.8%
ן24
11.8%
ה24
11.8%
ר12
 
5.9%
ל12
 
5.9%
ך12
 
5.9%
ס12
 
5.9%
ב12
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII4101053
98.6%
Cyrillic36913
 
0.9%
None19424
 
0.5%
Thai1869
 
< 0.1%
CJK751
 
< 0.1%
Katakana337
 
< 0.1%
Hebrew204
 
< 0.1%
Punctuation109
 
< 0.1%
Arabic64
 
< 0.1%
Hiragana32
 
< 0.1%
Other values (2)26
 
< 0.1%

Most frequent character per block

ValueCountFrequency (%)
'466586
 
11.4%
338844
 
8.3%
e275977
 
6.7%
a263891
 
6.4%
r203066
 
5.0%
n197300
 
4.8%
i194008
 
4.7%
o190382
 
4.6%
[174395
 
4.3%
]174395
 
4.3%
Other values (78)1622209
39.6%
ValueCountFrequency (%)
é4536
23.4%
á1226
 
6.3%
í907
 
4.7%
ó768
 
4.0%
ς755
 
3.9%
α720
 
3.7%
ö633
 
3.3%
ο521
 
2.7%
τ496
 
2.6%
ρ481
 
2.5%
Other values (122)8381
43.1%
ValueCountFrequency (%)
م14
21.9%
د9
14.1%
ي9
14.1%
ح7
10.9%
ف5
 
7.8%
و5
 
7.8%
ز5
 
7.8%
ا2
 
3.1%
ل2
 
3.1%
إ2
 
3.1%
Other values (2)4
 
6.2%
ValueCountFrequency (%)
р4600
12.5%
е3857
 
10.4%
и3390
 
9.2%
н2623
 
7.1%
м2437
 
6.6%
а2381
 
6.5%
Э2240
 
6.1%
т1407
 
3.8%
у1359
 
3.7%
с1246
 
3.4%
Other values (42)11373
30.8%
ValueCountFrequency (%)
35
 
4.7%
34
 
4.5%
30
 
4.0%
30
 
4.0%
29
 
3.9%
29
 
3.9%
27
 
3.6%
25
 
3.3%
24
 
3.2%
24
 
3.2%
Other values (207)464
61.8%
ValueCountFrequency (%)
ו48
23.5%
א24
11.8%
מ24
11.8%
ן24
11.8%
ה24
11.8%
ר12
 
5.9%
ל12
 
5.9%
ך12
 
5.9%
ס12
 
5.9%
ב12
 
5.9%
ValueCountFrequency (%)
34
 
10.1%
29
 
8.6%
16
 
4.7%
15
 
4.5%
15
 
4.5%
14
 
4.2%
14
 
4.2%
13
 
3.9%
12
 
3.6%
12
 
3.6%
Other values (40)163
48.4%
ValueCountFrequency (%)
40
36.7%
39
35.8%
26
23.9%
3
 
2.8%
1
 
0.9%
ValueCountFrequency (%)
146
 
7.8%
142
 
7.6%
138
 
7.4%
132
 
7.1%
115
 
6.2%
113
 
6.0%
103
 
5.5%
79
 
4.2%
74
 
4.0%
71
 
3.8%
Other values (41)756
40.4%
ValueCountFrequency (%)
2
 
8.0%
2
 
8.0%
2
 
8.0%
1
 
4.0%
1
 
4.0%
1
 
4.0%
1
 
4.0%
1
 
4.0%
1
 
4.0%
1
 
4.0%
Other values (12)12
48.0%
ValueCountFrequency (%)
11
34.4%
3
 
9.4%
3
 
9.4%
2
 
6.2%
2
 
6.2%
1
 
3.1%
1
 
3.1%
1
 
3.1%
1
 
3.1%
1
 
3.1%
Other values (6)6
18.8%
ValueCountFrequency (%)
1
100.0%

danceability
Real number (ℝ≥0)

Distinct1233
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5367575873
Minimum0
Maximum0.988
Zeros121
Zeros (%)0.1%
Memory size1.3 MiB
2022-09-30T21:41:06.681115image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.228
Q10.414
median0.548
Q30.669
95-th percentile0.807
Maximum0.988
Range0.988
Interquartile range (IQR)0.255

Descriptive statistics

Standard deviation0.1760252547
Coefficient of variation (CV)0.3279418099
Kurtosis-0.4694641663
Mean0.5367575873
Median Absolute Deviation (MAD)0.127
Skewness-0.2362339693
Sum93604.6189
Variance0.03098489029
MonotocityNot monotonic
2022-09-30T21:41:07.030884image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.565434
 
0.2%
0.612423
 
0.2%
0.545411
 
0.2%
0.546410
 
0.2%
0.578406
 
0.2%
0.628403
 
0.2%
0.632401
 
0.2%
0.528399
 
0.2%
0.559399
 
0.2%
0.629398
 
0.2%
Other values (1223)170305
97.7%
ValueCountFrequency (%)
0121
0.1%
0.05511
 
< 0.1%
0.05592
 
< 0.1%
0.05741
 
< 0.1%
0.05831
 
< 0.1%
0.05861
 
< 0.1%
0.05871
 
< 0.1%
0.05891
 
< 0.1%
0.0591
 
< 0.1%
0.05911
 
< 0.1%
ValueCountFrequency (%)
0.9881
 
< 0.1%
0.9871
 
< 0.1%
0.9861
 
< 0.1%
0.9853
< 0.1%
0.9821
 
< 0.1%
0.984
< 0.1%
0.9792
< 0.1%
0.9782
< 0.1%
0.9772
< 0.1%
0.9761
 
< 0.1%

duration_ms
Real number (ℝ≥0)

Distinct56306
Distinct (%)32.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean232810.032
Minimum4937
Maximum5338302
Zeros0
Zeros (%)0.0%
Memory size1.3 MiB
2022-09-30T21:41:07.172823image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum4937
5-th percentile103043.2
Q1166133
median205787
Q3265720
95-th percentile430916.8
Maximum5338302
Range5333365
Interquartile range (IQR)99587

Descriptive statistics

Standard deviation148395.7977
Coefficient of variation (CV)0.6374115256
Kurtosis216.6658797
Mean232810.032
Median Absolute Deviation (MAD)47640
Skewness10.15938356
Sum4.059950868 × 1010
Variance2.202131277 × 1010
MonotocityNot monotonic
2022-09-30T21:41:07.285824image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18000054
 
< 0.1%
15000054
 
< 0.1%
19200053
 
< 0.1%
25300052
 
< 0.1%
18600051
 
< 0.1%
17500049
 
< 0.1%
20800048
 
< 0.1%
24000048
 
< 0.1%
16800047
 
< 0.1%
19500046
 
< 0.1%
Other values (56296)173887
99.7%
ValueCountFrequency (%)
49371
< 0.1%
51081
< 0.1%
59911
< 0.1%
62501
< 0.1%
63621
< 0.1%
64671
< 0.1%
69371
< 0.1%
80421
< 0.1%
88532
< 0.1%
90531
< 0.1%
ValueCountFrequency (%)
53383021
< 0.1%
50421851
< 0.1%
48927612
< 0.1%
48001182
< 0.1%
47925871
< 0.1%
47374581
< 0.1%
46966901
< 0.1%
46859271
< 0.1%
46757101
< 0.1%
45901061
< 0.1%

energy
Real number (ℝ≥0)

Distinct2306
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4827208433
Minimum0
Maximum1
Zeros7
Zeros (%)< 0.1%
Memory size1.3 MiB
2022-09-30T21:41:07.402467image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0751
Q10.249
median0.465
Q30.711
95-th percentile0.934
Maximum1
Range1
Interquartile range (IQR)0.462

Descriptive statistics

Standard deviation0.2726854799
Coefficient of variation (CV)0.5648926987
Kurtosis-1.12421945
Mean0.4827208433
Median Absolute Deviation (MAD)0.229
Skewness0.144279154
Sum84181.20515
Variance0.07435737094
MonotocityNot monotonic
2022-09-30T21:41:07.514820image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.195271
 
0.2%
0.201265
 
0.2%
0.245256
 
0.1%
0.254256
 
0.1%
0.23255
 
0.1%
0.187254
 
0.1%
0.263254
 
0.1%
0.219250
 
0.1%
0.225249
 
0.1%
0.259248
 
0.1%
Other values (2296)171831
98.5%
ValueCountFrequency (%)
07
< 0.1%
1.99 × 1051
 
< 0.1%
2.01 × 1053
< 0.1%
2.02 × 1051
 
< 0.1%
2.03 × 1053
< 0.1%
2.8 × 1051
 
< 0.1%
4.28 × 1051
 
< 0.1%
4.98 × 1051
 
< 0.1%
6.19 × 1052
 
< 0.1%
7.46 × 1051
 
< 0.1%
ValueCountFrequency (%)
131
 
< 0.1%
0.99970
< 0.1%
0.99892
0.1%
0.997106
0.1%
0.996107
0.1%
0.995126
0.1%
0.994102
0.1%
0.993116
0.1%
0.99290
0.1%
0.991123
0.1%

explicit
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.6 MiB
0
162507 
1
 
11882

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters174389
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1
ValueCountFrequency (%)
0162507
93.2%
111882
 
6.8%
2022-09-30T21:41:07.715934image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2022-09-30T21:41:07.781935image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0162507
93.2%
111882
 
6.8%

Most occurring characters

ValueCountFrequency (%)
0162507
93.2%
111882
 
6.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number174389
100.0%

Most frequent character per category

ValueCountFrequency (%)
0162507
93.2%
111882
 
6.8%

Most occurring scripts

ValueCountFrequency (%)
Common174389
100.0%

Most frequent character per script

ValueCountFrequency (%)
0162507
93.2%
111882
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII174389
100.0%

Most frequent character per block

ValueCountFrequency (%)
0162507
93.2%
111882
 
6.8%

id
Categorical

HIGH CARDINALITY
UNIFORM

Distinct172230
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Memory size13.1 MiB
0UsmyJDsst2xhX1ZiFF3JW
 
9
7tJS1cjSD1P8bodNGblYiK
 
9
1xQvPFljQXA3GCK869ERvC
 
9
2pZDhsmGRSkRgWNfkDr70S
 
8
7GlixhQpXo76vgALqoJ3L5
 
8
Other values (172225)
174346 

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters3836558
Distinct characters62
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique170337 ?
Unique (%)97.7%

Sample

1st row0cS0A1fUEUd1EW3FcF8AEI
2nd row0hbkKFIJm7Z05H8Zl9w30f
3rd row11m7laMUgmOKqI3oYzuhne
4th row19Lc5SfJJ5O1oaxY0fpwfh
5th row2hJjbsLCytGsnAHfdsLejp
ValueCountFrequency (%)
0UsmyJDsst2xhX1ZiFF3JW9
 
< 0.1%
7tJS1cjSD1P8bodNGblYiK9
 
< 0.1%
1xQvPFljQXA3GCK869ERvC9
 
< 0.1%
2pZDhsmGRSkRgWNfkDr70S8
 
< 0.1%
7GlixhQpXo76vgALqoJ3L58
 
< 0.1%
4gpjBYc0lAkhYnmnfMeq4g8
 
< 0.1%
7tH6tGz6cQtpYReqHTlyjN8
 
< 0.1%
7Kh32CyazzTdVEBXjKINVO8
 
< 0.1%
7FmPp4BjDVaEwhsEO7B7Oe7
 
< 0.1%
4OtTVS1297d8h56MAX4Wpy7
 
< 0.1%
Other values (172220)174308
> 99.9%
2022-09-30T21:41:08.432434image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
7tjs1cjsd1p8bodngblyik9
 
< 0.1%
0usmyjdsst2xhx1ziff3jw9
 
< 0.1%
1xqvpfljqxa3gck869ervc9
 
< 0.1%
7th6tgz6cqtpyreqhtlyjn8
 
< 0.1%
4gpjbyc0lakhynmnfmeq4g8
 
< 0.1%
7glixhqpxo76vgalqoj3l58
 
< 0.1%
7kh32cyazztdvebxjkinvo8
 
< 0.1%
2pzdhsmgrskrgwnfkdr70s8
 
< 0.1%
4ottvs1297d8h56max4wpy7
 
< 0.1%
5rjw9vspndfnv9ar97xzg27
 
< 0.1%
Other values (172220)174308
> 99.9%

Most occurring characters

ValueCountFrequency (%)
084657
 
2.2%
183184
 
2.2%
282047
 
2.1%
381141
 
2.1%
480711
 
2.1%
580445
 
2.1%
679504
 
2.1%
775259
 
2.0%
L59735
 
1.6%
D59588
 
1.6%
Other values (52)3070287
80.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1537109
40.1%
Uppercase Letter1533915
40.0%
Decimal Number765534
20.0%

Most frequent character per category

ValueCountFrequency (%)
t59472
 
3.9%
a59379
 
3.9%
l59324
 
3.9%
b59320
 
3.9%
s59303
 
3.9%
d59231
 
3.9%
y59221
 
3.9%
p59215
 
3.9%
e59206
 
3.9%
q59192
 
3.9%
Other values (16)944246
61.4%
ValueCountFrequency (%)
L59735
 
3.9%
D59588
 
3.9%
F59505
 
3.9%
Q59503
 
3.9%
C59466
 
3.9%
J59413
 
3.9%
M59208
 
3.9%
I59135
 
3.9%
H59103
 
3.9%
T59064
 
3.9%
Other values (16)940195
61.3%
ValueCountFrequency (%)
084657
11.1%
183184
10.9%
282047
10.7%
381141
10.6%
480711
10.5%
580445
10.5%
679504
10.4%
775259
9.8%
959394
7.8%
859192
7.7%

Most occurring scripts

ValueCountFrequency (%)
Latin3071024
80.0%
Common765534
 
20.0%

Most frequent character per script

ValueCountFrequency (%)
L59735
 
1.9%
D59588
 
1.9%
F59505
 
1.9%
Q59503
 
1.9%
t59472
 
1.9%
C59466
 
1.9%
J59413
 
1.9%
a59379
 
1.9%
l59324
 
1.9%
b59320
 
1.9%
Other values (42)2476319
80.6%
ValueCountFrequency (%)
084657
11.1%
183184
10.9%
282047
10.7%
381141
10.6%
480711
10.5%
580445
10.5%
679504
10.4%
775259
9.8%
959394
7.8%
859192
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII3836558
100.0%

Most frequent character per block

ValueCountFrequency (%)
084657
 
2.2%
183184
 
2.2%
282047
 
2.1%
381141
 
2.1%
480711
 
2.1%
580445
 
2.1%
679504
 
2.1%
775259
 
2.0%
L59735
 
1.6%
D59588
 
1.6%
Other values (52)3070287
80.0%

instrumentalness
Real number (ℝ≥0)

ZEROS

Distinct5400
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1972520469
Minimum0
Maximum1
Zeros43652
Zeros (%)25.0%
Memory size1.3 MiB
2022-09-30T21:41:08.559420image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.000524
Q30.252
95-th percentile0.913
Maximum1
Range1
Interquartile range (IQR)0.252

Descriptive statistics

Standard deviation0.3345737053
Coefficient of variation (CV)1.696173553
Kurtosis0.1008415577
Mean0.1972520469
Median Absolute Deviation (MAD)0.000524
Skewness1.36370426
Sum34398.58721
Variance0.1119395643
MonotocityNot monotonic
2022-09-30T21:41:08.671426image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
043652
 
25.0%
0.917250
 
0.1%
0.911243
 
0.1%
0.905237
 
0.1%
0.916232
 
0.1%
0.901232
 
0.1%
0.904231
 
0.1%
0.913221
 
0.1%
0.909218
 
0.1%
0.914217
 
0.1%
Other values (5390)128656
73.8%
ValueCountFrequency (%)
043652
25.0%
1 × 10633
 
< 0.1%
1.01 × 10664
 
< 0.1%
1.02 × 10674
 
< 0.1%
1.03 × 10666
 
< 0.1%
1.04 × 10650
 
< 0.1%
1.05 × 10661
 
< 0.1%
1.06 × 10651
 
< 0.1%
1.07 × 10660
 
< 0.1%
1.08 × 10657
 
< 0.1%
ValueCountFrequency (%)
17
< 0.1%
0.9998
< 0.1%
0.9986
 
< 0.1%
0.9975
 
< 0.1%
0.9966
 
< 0.1%
0.9956
 
< 0.1%
0.99411
< 0.1%
0.99315
< 0.1%
0.9928
< 0.1%
0.9918
< 0.1%

key
Real number (ℝ≥0)

ZEROS

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.20530538
Minimum0
Maximum11
Zeros21967
Zeros (%)12.6%
Memory size1.3 MiB
2022-09-30T21:41:08.768085image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q38
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.518291808
Coefficient of variation (CV)0.675904976
Kurtosis-1.27203423
Mean5.20530538
Median Absolute Deviation (MAD)3
Skewness0.003931508987
Sum907748
Variance12.37837725
MonotocityNot monotonic
2022-09-30T21:41:08.842636image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
021967
12.6%
721363
12.3%
218916
10.8%
918109
10.4%
516546
9.5%
113562
7.8%
413327
7.6%
1012406
7.1%
1111014
6.3%
810673
6.1%
Other values (2)16506
9.5%
ValueCountFrequency (%)
021967
12.6%
113562
7.8%
218916
10.8%
37280
 
4.2%
413327
7.6%
516546
9.5%
69226
5.3%
721363
12.3%
810673
6.1%
918109
10.4%
ValueCountFrequency (%)
1111014
6.3%
1012406
7.1%
918109
10.4%
810673
6.1%
721363
12.3%
69226
5.3%
516546
9.5%
413327
7.6%
37280
 
4.2%
218916
10.8%

liveness
Real number (ℝ≥0)

Distinct1740
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2111231446
Minimum0
Maximum1
Zeros13
Zeros (%)< 0.1%
Memory size1.3 MiB
2022-09-30T21:41:08.940505image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0596
Q10.0992
median0.138
Q30.27
95-th percentile0.638
Maximum1
Range1
Interquartile range (IQR)0.1708

Descriptive statistics

Standard deviation0.1804927149
Coefficient of variation (CV)0.8549167604
Kurtosis4.495865735
Mean0.2111231446
Median Absolute Deviation (MAD)0.0557
Skewness2.078373514
Sum36817.55407
Variance0.03257762012
MonotocityNot monotonic
2022-09-30T21:41:09.058015image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1111817
 
1.0%
0.111662
 
1.0%
0.1091630
 
0.9%
0.1081603
 
0.9%
0.1071547
 
0.9%
0.1051504
 
0.9%
0.1121499
 
0.9%
0.1061496
 
0.9%
0.1131362
 
0.8%
0.1021349
 
0.8%
Other values (1730)158920
91.1%
ValueCountFrequency (%)
013
< 0.1%
0.009671
 
< 0.1%
0.01011
 
< 0.1%
0.01031
 
< 0.1%
0.01161
 
< 0.1%
0.01191
 
< 0.1%
0.0121
 
< 0.1%
0.01362
 
< 0.1%
0.01391
 
< 0.1%
0.01421
 
< 0.1%
ValueCountFrequency (%)
11
 
< 0.1%
0.9991
 
< 0.1%
0.9981
 
< 0.1%
0.9974
 
< 0.1%
0.9964
 
< 0.1%
0.9958
 
< 0.1%
0.99411
< 0.1%
0.9937
 
< 0.1%
0.99214
< 0.1%
0.99120
< 0.1%

loudness
Real number (ℝ)

Distinct25580
Distinct (%)14.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-11.75086499
Minimum-60
Maximum3.855
Zeros0
Zeros (%)0.0%
Memory size1.3 MiB
2022-09-30T21:41:09.178015image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-60
5-th percentile-22.4816
Q1-14.908
median-10.836
Q3-7.499
95-th percentile-4.348
Maximum3.855
Range63.855
Interquartile range (IQR)7.409

Descriptive statistics

Standard deviation5.691590819
Coefficient of variation (CV)-0.4843550516
Kurtosis1.491533022
Mean-11.75086499
Median Absolute Deviation (MAD)3.623
Skewness-0.9891197103
Sum-2049221.595
Variance32.39420605
MonotocityNot monotonic
2022-09-30T21:41:09.291564image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-7.57848
 
< 0.1%
-8.70540
 
< 0.1%
-3.30435
 
< 0.1%
-10.3432
 
< 0.1%
-7.03131
 
< 0.1%
-5.70231
 
< 0.1%
-8.99230
 
< 0.1%
-8.98630
 
< 0.1%
-10.43829
 
< 0.1%
-7.60128
 
< 0.1%
Other values (25570)174055
99.8%
ValueCountFrequency (%)
-607
< 0.1%
-551
 
< 0.1%
-54.3761
 
< 0.1%
-48.5871
 
< 0.1%
-48.2782
 
< 0.1%
-47.7311
 
< 0.1%
-47.0461
 
< 0.1%
-46.8251
 
< 0.1%
-45.3531
 
< 0.1%
-44.7611
 
< 0.1%
ValueCountFrequency (%)
3.8551
 
< 0.1%
3.7441
 
< 0.1%
3.3671
 
< 0.1%
1.9631
 
< 0.1%
1.831
 
< 0.1%
1.3421
 
< 0.1%
1.2751
 
< 0.1%
1.0275
< 0.1%
1.0231
 
< 0.1%
1.0061
 
< 0.1%

mode
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.6 MiB
1
122488 
0
51901 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters174389
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row0
ValueCountFrequency (%)
1122488
70.2%
051901
29.8%
2022-09-30T21:41:09.505685image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2022-09-30T21:41:09.566246image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1122488
70.2%
051901
29.8%

Most occurring characters

ValueCountFrequency (%)
1122488
70.2%
051901
29.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number174389
100.0%

Most frequent character per category

ValueCountFrequency (%)
1122488
70.2%
051901
29.8%

Most occurring scripts

ValueCountFrequency (%)
Common174389
100.0%

Most frequent character per script

ValueCountFrequency (%)
1122488
70.2%
051901
29.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII174389
100.0%

Most frequent character per block

ValueCountFrequency (%)
1122488
70.2%
051901
29.8%

name
Categorical

HIGH CARDINALITY

Distinct137013
Distinct (%)78.6%
Missing0
Missing (%)0.0%
Memory size14.1 MiB
White Christmas
 
103
Winter Wonderland
 
88
Silent Night
 
81
Jingle Bells
 
71
2000 Years
 
70
Other values (137008)
173976 

Length

Max length255
Median length20
Mean length24.93034538
Min length1

Characters and Unicode

Total characters4347578
Distinct characters1508
Distinct categories21 ?
Distinct scripts13 ?
Distinct blocks20 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique117624 ?
Unique (%)67.4%

Sample

1st rowKeep A Song In Your Soul
2nd rowI Put A Spell On You
3rd rowGolfing Papa
4th rowTrue House Music - Xavier Santos & Carlos Gomix Remix
5th rowXuniverxe
ValueCountFrequency (%)
White Christmas103
 
0.1%
Winter Wonderland88
 
0.1%
Silent Night81
 
< 0.1%
Jingle Bells71
 
< 0.1%
2000 Years70
 
< 0.1%
Happy New Year57
 
< 0.1%
Sleigh Ride54
 
< 0.1%
Summertime53
 
< 0.1%
The Christmas Song51
 
< 0.1%
Silver Bells51
 
< 0.1%
Other values (137003)173710
99.6%
2022-09-30T21:41:10.087411image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
48517
 
6.0%
the23495
 
2.9%
in11698
 
1.4%
a9315
 
1.1%
i9009
 
1.1%
you8994
 
1.1%
of8802
 
1.1%
no6584
 
0.8%
me6493
 
0.8%
remaster6162
 
0.8%
Other values (62717)675868
82.9%

Most occurring characters

ValueCountFrequency (%)
640548
 
14.7%
e373593
 
8.6%
a267401
 
6.2%
o241753
 
5.6%
i207908
 
4.8%
n201722
 
4.6%
r197181
 
4.5%
t182239
 
4.2%
s133597
 
3.1%
l124717
 
2.9%
Other values (1498)1776919
40.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2706116
62.2%
Uppercase Letter662228
 
15.2%
Space Separator640548
 
14.7%
Decimal Number133082
 
3.1%
Other Punctuation111640
 
2.6%
Dash Punctuation45873
 
1.1%
Close Punctuation18540
 
0.4%
Open Punctuation18489
 
0.4%
Other Letter8914
 
0.2%
Nonspacing Mark1075
 
< 0.1%
Other values (11)1073
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
328
 
3.7%
279
 
3.1%
253
 
2.8%
249
 
2.8%
234
 
2.6%
230
 
2.6%
225
 
2.5%
188
 
2.1%
158
 
1.8%
155
 
1.7%
Other values (1122)6615
74.2%
ValueCountFrequency (%)
e373593
13.8%
a267401
 
9.9%
o241753
 
8.9%
i207908
 
7.7%
n201722
 
7.5%
r197181
 
7.3%
t182239
 
6.7%
s133597
 
4.9%
l124717
 
4.6%
h95666
 
3.5%
Other values (157)680339
25.1%
ValueCountFrequency (%)
T52186
 
7.9%
M51698
 
7.8%
S50874
 
7.7%
A40879
 
6.2%
I39397
 
5.9%
L36849
 
5.6%
R36847
 
5.6%
C35612
 
5.4%
B34090
 
5.1%
D30673
 
4.6%
Other values (97)253123
38.2%
ValueCountFrequency (%)
.31547
28.3%
,23383
20.9%
'21350
19.1%
:13769
12.3%
"7655
 
6.9%
/5002
 
4.5%
&3904
 
3.5%
!1998
 
1.8%
?1457
 
1.3%
;771
 
0.7%
Other values (14)804
 
0.7%
ValueCountFrequency (%)
277
25.8%
195
18.1%
184
17.1%
108
 
10.0%
76
 
7.1%
50
 
4.7%
41
 
3.8%
34
 
3.2%
33
 
3.1%
32
 
3.0%
Other values (5)45
 
4.2%
ValueCountFrequency (%)
226723
20.1%
126708
20.1%
025319
19.0%
910375
 
7.8%
38803
 
6.6%
57807
 
5.9%
47775
 
5.8%
66569
 
4.9%
86568
 
4.9%
76435
 
4.8%
ValueCountFrequency (%)
+210
74.2%
=29
 
10.2%
~19
 
6.7%
>12
 
4.2%
|4
 
1.4%
<4
 
1.4%
4
 
1.4%
1
 
0.4%
ValueCountFrequency (%)
(17204
93.0%
[1266
 
6.8%
7
 
< 0.1%
6
 
< 0.1%
{4
 
< 0.1%
1
 
< 0.1%
1
 
< 0.1%
ValueCountFrequency (%)
)17253
93.1%
]1267
 
6.8%
7
 
< 0.1%
6
 
< 0.1%
}5
 
< 0.1%
1
 
< 0.1%
1
 
< 0.1%
ValueCountFrequency (%)
-45798
99.8%
55
 
0.1%
13
 
< 0.1%
5
 
< 0.1%
2
 
< 0.1%
ValueCountFrequency (%)
104
94.5%
4
 
3.6%
ʻ1
 
0.9%
1
 
0.9%
ValueCountFrequency (%)
°12
75.0%
®2
 
12.5%
1
 
6.2%
1
 
6.2%
ValueCountFrequency (%)
1
25.0%
³1
25.0%
¹1
25.0%
²1
25.0%
ValueCountFrequency (%)
293
76.7%
85
 
22.3%
»4
 
1.0%
ValueCountFrequency (%)
82
78.1%
19
 
18.1%
«4
 
3.8%
ValueCountFrequency (%)
´24
92.3%
`2
 
7.7%
ValueCountFrequency (%)
$108
98.2%
£2
 
1.8%
ValueCountFrequency (%)
640548
100.0%
ValueCountFrequency (%)
_34
100.0%
ValueCountFrequency (%)
2
100.0%
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin3302607
76.0%
Common969239
 
22.3%
Cyrillic57128
 
1.3%
Greek8596
 
0.2%
Thai5176
 
0.1%
Han2690
 
0.1%
Katakana851
 
< 0.1%
Hiragana435
 
< 0.1%
Hebrew406
 
< 0.1%
Arabic236
 
< 0.1%
Other values (3)214
 
< 0.1%

Most frequent character per script

ValueCountFrequency (%)
54
 
2.0%
54
 
2.0%
51
 
1.9%
49
 
1.8%
41
 
1.5%
36
 
1.3%
35
 
1.3%
32
 
1.2%
32
 
1.2%
30
 
1.1%
Other values (775)2276
84.6%
ValueCountFrequency (%)
e373593
 
11.3%
a267401
 
8.1%
o241753
 
7.3%
i207908
 
6.3%
n201722
 
6.1%
r197181
 
6.0%
t182239
 
5.5%
s133597
 
4.0%
l124717
 
3.8%
h95666
 
2.9%
Other values (143)1276830
38.7%
ValueCountFrequency (%)
4
 
2.4%
4
 
2.4%
4
 
2.4%
4
 
2.4%
3
 
1.8%
3
 
1.8%
3
 
1.8%
3
 
1.8%
3
 
1.8%
3
 
1.8%
Other values (103)135
79.9%
ValueCountFrequency (%)
640548
66.1%
-45798
 
4.7%
.31547
 
3.3%
226723
 
2.8%
126708
 
2.8%
025319
 
2.6%
,23383
 
2.4%
'21350
 
2.2%
)17253
 
1.8%
(17204
 
1.8%
Other values (74)93406
 
9.6%
ValueCountFrequency (%)
53
 
6.2%
51
 
6.0%
47
 
5.5%
39
 
4.6%
35
 
4.1%
32
 
3.8%
31
 
3.6%
28
 
3.3%
24
 
2.8%
24
 
2.8%
Other values (63)487
57.2%
ValueCountFrequency (%)
α910
 
10.6%
ι614
 
7.1%
ο610
 
7.1%
τ500
 
5.8%
ν486
 
5.7%
ρ399
 
4.6%
ε380
 
4.4%
μ332
 
3.9%
λ324
 
3.8%
ά323
 
3.8%
Other values (52)3718
43.3%
ValueCountFrequency (%)
55
 
12.6%
32
 
7.4%
23
 
5.3%
19
 
4.4%
18
 
4.1%
17
 
3.9%
15
 
3.4%
15
 
3.4%
14
 
3.2%
14
 
3.2%
Other values (51)213
49.0%
ValueCountFrequency (%)
328
 
6.3%
279
 
5.4%
277
 
5.4%
253
 
4.9%
249
 
4.8%
234
 
4.5%
230
 
4.4%
225
 
4.3%
195
 
3.8%
188
 
3.6%
Other values (48)2718
52.5%
ValueCountFrequency (%)
а7338
12.8%
т5005
 
8.8%
ь4141
 
7.2%
о4080
 
7.1%
с3670
 
6.4%
е3335
 
5.8%
Ч3233
 
5.7%
р3106
 
5.4%
н2533
 
4.4%
и2424
 
4.2%
Other values (45)18263
32.0%
ValueCountFrequency (%)
ا37
15.7%
ي25
 
10.6%
ل24
 
10.2%
و15
 
6.4%
ب13
 
5.5%
ن13
 
5.5%
م12
 
5.1%
ى9
 
3.8%
ع9
 
3.8%
د8
 
3.4%
Other values (19)71
30.1%
ValueCountFrequency (%)
י53
13.1%
ו43
 
10.6%
ה41
 
10.1%
ל30
 
7.4%
ב26
 
6.4%
ש21
 
5.2%
ר20
 
4.9%
א19
 
4.7%
ת14
 
3.4%
ן14
 
3.4%
Other values (16)125
30.8%
ValueCountFrequency (%)
6
33.3%
4
22.2%
2
 
11.1%
2
 
11.1%
2
 
11.1%
2
 
11.1%
ValueCountFrequency (%)
́21
77.8%
4
 
14.8%
̃2
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII4258803
98.0%
Cyrillic57128
 
1.3%
None20854
 
0.5%
Thai5176
 
0.1%
CJK2689
 
0.1%
Katakana1023
 
< 0.1%
Punctuation592
 
< 0.1%
Hiragana439
 
< 0.1%
Hebrew406
 
< 0.1%
Arabic236
 
< 0.1%
Other values (10)232
 
< 0.1%

Most frequent character per block

ValueCountFrequency (%)
640548
 
15.0%
e373593
 
8.8%
a267401
 
6.3%
o241753
 
5.7%
i207908
 
4.9%
n201722
 
4.7%
r197181
 
4.6%
t182239
 
4.3%
s133597
 
3.1%
l124717
 
2.9%
Other values (84)1688144
39.6%
ValueCountFrequency (%)
é2334
 
11.2%
í1129
 
5.4%
ó974
 
4.7%
α910
 
4.4%
á830
 
4.0%
ñ689
 
3.3%
è643
 
3.1%
ι614
 
2.9%
ο610
 
2.9%
ü553
 
2.7%
Other values (167)11568
55.5%
ValueCountFrequency (%)
ا37
15.7%
ي25
 
10.6%
ل24
 
10.2%
و15
 
6.4%
ب13
 
5.5%
ن13
 
5.5%
م12
 
5.1%
ى9
 
3.8%
ع9
 
3.8%
د8
 
3.4%
Other values (19)71
30.1%
ValueCountFrequency (%)
а7338
12.8%
т5005
 
8.8%
ь4141
 
7.2%
о4080
 
7.1%
с3670
 
6.4%
е3335
 
5.8%
Ч3233
 
5.7%
р3106
 
5.4%
н2533
 
4.4%
и2424
 
4.2%
Other values (45)18263
32.0%
ValueCountFrequency (%)
293
49.5%
85
 
14.4%
82
 
13.9%
55
 
9.3%
44
 
7.4%
19
 
3.2%
5
 
0.8%
2
 
0.3%
2
 
0.3%
2
 
0.3%
Other values (2)3
 
0.5%
ValueCountFrequency (%)
54
 
2.0%
54
 
2.0%
51
 
1.9%
49
 
1.8%
41
 
1.5%
36
 
1.3%
35
 
1.3%
32
 
1.2%
32
 
1.2%
30
 
1.1%
Other values (774)2275
84.6%
ValueCountFrequency (%)
328
 
6.3%
279
 
5.4%
277
 
5.4%
253
 
4.9%
249
 
4.8%
234
 
4.5%
230
 
4.4%
225
 
4.3%
195
 
3.8%
188
 
3.6%
Other values (48)2718
52.5%
ValueCountFrequency (%)
55
 
12.5%
32
 
7.3%
23
 
5.2%
19
 
4.3%
18
 
4.1%
17
 
3.9%
15
 
3.4%
15
 
3.4%
14
 
3.2%
14
 
3.2%
Other values (52)217
49.4%
ValueCountFrequency (%)
104
 
10.2%
68
 
6.6%
53
 
5.2%
51
 
5.0%
47
 
4.6%
39
 
3.8%
35
 
3.4%
32
 
3.1%
31
 
3.0%
28
 
2.7%
Other values (65)535
52.3%
ValueCountFrequency (%)
ế2
11.8%
2
11.8%
2
11.8%
2
11.8%
2
11.8%
2
11.8%
2
11.8%
1
5.9%
1
5.9%
1
5.9%
ValueCountFrequency (%)
4
 
2.6%
4
 
2.6%
4
 
2.6%
4
 
2.6%
3
 
1.9%
3
 
1.9%
3
 
1.9%
3
 
1.9%
3
 
1.9%
3
 
1.9%
Other values (91)120
77.9%
ValueCountFrequency (%)
́21
91.3%
̃2
 
8.7%
ValueCountFrequency (%)
1
100.0%
ValueCountFrequency (%)
6
33.3%
4
22.2%
2
 
11.1%
2
 
11.1%
2
 
11.1%
2
 
11.1%
ValueCountFrequency (%)
י53
13.1%
ו43
 
10.6%
ה41
 
10.1%
ל30
 
7.4%
ב26
 
6.4%
ש21
 
5.2%
ר20
 
4.9%
א19
 
4.7%
ת14
 
3.4%
ן14
 
3.4%
Other values (16)125
30.8%
ValueCountFrequency (%)
ʻ1
100.0%
ValueCountFrequency (%)
1
100.0%
ValueCountFrequency (%)
1
100.0%
ValueCountFrequency (%)
1
100.0%
ValueCountFrequency (%)
3
20.0%
2
13.3%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
Other values (2)2
13.3%

popularity
Real number (ℝ≥0)

ZEROS

Distinct98
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.69338089
Minimum0
Maximum100
Zeros40905
Zeros (%)23.5%
Memory size1.3 MiB
2022-09-30T21:41:10.225300image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median25
Q342
95-th percentile64
Maximum100
Range100
Interquartile range (IQR)41

Descriptive statistics

Standard deviation21.87273983
Coefficient of variation (CV)0.8512986251
Kurtosis-0.946212195
Mean25.69338089
Median Absolute Deviation (MAD)20
Skewness0.362937464
Sum4480643
Variance478.4167475
MonotocityNot monotonic
2022-09-30T21:41:10.337713image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
040905
 
23.5%
13846
 
2.2%
332819
 
1.6%
342790
 
1.6%
362784
 
1.6%
322737
 
1.6%
352727
 
1.6%
312648
 
1.5%
302609
 
1.5%
382600
 
1.5%
Other values (88)107924
61.9%
ValueCountFrequency (%)
040905
23.5%
13846
 
2.2%
22218
 
1.3%
31903
 
1.1%
41652
 
0.9%
51650
 
0.9%
61561
 
0.9%
71652
 
0.9%
81629
 
0.9%
91698
 
1.0%
ValueCountFrequency (%)
1001
 
< 0.1%
962
 
< 0.1%
952
 
< 0.1%
945
 
< 0.1%
932
 
< 0.1%
926
 
< 0.1%
9113
< 0.1%
9013
< 0.1%
8918
< 0.1%
8814
< 0.1%

release_date
Categorical

HIGH CARDINALITY

Distinct11043
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Memory size10.8 MiB
1945
 
1447
1949
 
1234
1948
 
1172
1935
 
1105
1926
 
1059
Other values (11038)
168372 

Length

Max length10
Median length10
Mean length8.224245795
Min length4

Characters and Unicode

Total characters1434218
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2955 ?
Unique (%)1.7%

Sample

1st row1920
2nd row1920-01-05
3rd row1920
4th row1920-01-01
5th row1920-10-01
ValueCountFrequency (%)
19451447
 
0.8%
19491234
 
0.7%
19481172
 
0.7%
19351105
 
0.6%
19261059
 
0.6%
19501012
 
0.6%
19461001
 
0.6%
1930-01-01992
 
0.6%
1940-01-01978
 
0.6%
1951967
 
0.6%
Other values (11033)163422
93.7%
2022-09-30T21:41:10.595316image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
19451447
 
0.8%
19491234
 
0.7%
19481172
 
0.7%
19351105
 
0.6%
19261059
 
0.6%
19501012
 
0.6%
19461001
 
0.6%
1930-01-01992
 
0.6%
1940-01-01978
 
0.6%
1951967
 
0.6%
Other values (11033)163422
93.7%

Most occurring characters

ValueCountFrequency (%)
1335543
23.4%
-245554
17.1%
0244445
17.0%
9184833
12.9%
2125150
 
8.7%
652806
 
3.7%
552668
 
3.7%
851451
 
3.6%
749930
 
3.5%
348319
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1188664
82.9%
Dash Punctuation245554
 
17.1%

Most frequent character per category

ValueCountFrequency (%)
1335543
28.2%
0244445
20.6%
9184833
15.5%
2125150
 
10.5%
652806
 
4.4%
552668
 
4.4%
851451
 
4.3%
749930
 
4.2%
348319
 
4.1%
443519
 
3.7%
ValueCountFrequency (%)
-245554
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1434218
100.0%

Most frequent character per script

ValueCountFrequency (%)
1335543
23.4%
-245554
17.1%
0244445
17.0%
9184833
12.9%
2125150
 
8.7%
652806
 
3.7%
552668
 
3.7%
851451
 
3.6%
749930
 
3.5%
348319
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII1434218
100.0%

Most frequent character per block

ValueCountFrequency (%)
1335543
23.4%
-245554
17.1%
0244445
17.0%
9184833
12.9%
2125150
 
8.7%
652806
 
3.7%
552668
 
3.7%
851451
 
3.6%
749930
 
3.5%
348319
 
3.4%

speechiness
Real number (ℝ≥0)

Distinct1633
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1057291836
Minimum0
Maximum0.971
Zeros121
Zeros (%)0.1%
Memory size1.3 MiB
2022-09-30T21:41:10.712895image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0282
Q10.0352
median0.0455
Q30.0763
95-th percentile0.414
Maximum0.971
Range0.971
Interquartile range (IQR)0.0411

Descriptive statistics

Standard deviation0.1822601819
Coefficient of variation (CV)1.723839868
Kurtosis13.7766551
Mean0.1057291836
Median Absolute Deviation (MAD)0.0133
Skewness3.750970756
Sum18438.0066
Variance0.03321877392
MonotocityNot monotonic
2022-09-30T21:41:10.833785image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0337581
 
0.3%
0.0334574
 
0.3%
0.0347571
 
0.3%
0.033568
 
0.3%
0.0362565
 
0.3%
0.0363563
 
0.3%
0.0319559
 
0.3%
0.0328558
 
0.3%
0.0352557
 
0.3%
0.0366554
 
0.3%
Other values (1623)168739
96.8%
ValueCountFrequency (%)
0121
0.1%
0.02221
 
< 0.1%
0.02233
 
< 0.1%
0.02246
 
< 0.1%
0.02254
 
< 0.1%
0.02265
 
< 0.1%
0.02277
 
< 0.1%
0.02289
 
< 0.1%
0.02296
 
< 0.1%
0.0234
 
< 0.1%
ValueCountFrequency (%)
0.9711
 
< 0.1%
0.973
 
< 0.1%
0.96910
 
< 0.1%
0.96811
 
< 0.1%
0.96729
 
< 0.1%
0.96668
 
< 0.1%
0.965108
0.1%
0.964126
0.1%
0.963194
0.1%
0.962201
0.1%

tempo
Real number (ℝ≥0)

Distinct84123
Distinct (%)48.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean117.0064998
Minimum0
Maximum243.507
Zeros121
Zeros (%)0.1%
Memory size1.3 MiB
2022-09-30T21:41:10.971400image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile74.103
Q193.931
median115.816
Q3135.011
95-th percentile173.938
Maximum243.507
Range243.507
Interquartile range (IQR)41.08

Descriptive statistics

Standard deviation30.25417803
Coefficient of variation (CV)0.2585683537
Kurtosis-0.03662102657
Mean117.0064998
Median Absolute Deviation (MAD)20.718
Skewness0.42339438
Sum20404646.49
Variance915.315288
MonotocityNot monotonic
2022-09-30T21:41:11.085247image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0121
 
0.1%
128.00357
 
< 0.1%
128.0146
 
< 0.1%
12844
 
< 0.1%
127.99841
 
< 0.1%
127.99140
 
< 0.1%
128.00840
 
< 0.1%
130.00738
 
< 0.1%
127.99937
 
< 0.1%
128.01336
 
< 0.1%
Other values (84113)173889
99.7%
ValueCountFrequency (%)
0121
0.1%
30.9461
 
< 0.1%
31.9881
 
< 0.1%
32.5711
 
< 0.1%
32.81
 
< 0.1%
32.9411
 
< 0.1%
33.3341
 
< 0.1%
33.3911
 
< 0.1%
33.9441
 
< 0.1%
34.4961
 
< 0.1%
ValueCountFrequency (%)
243.5071
< 0.1%
243.3721
< 0.1%
238.8951
< 0.1%
236.7991
< 0.1%
224.4371
< 0.1%
222.6051
< 0.1%
221.9541
< 0.1%
221.7411
< 0.1%
221.1121
< 0.1%
221.0582
< 0.1%

valence
Real number (ℝ≥0)

Distinct1707
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5245326904
Minimum0
Maximum1
Zeros173
Zeros (%)0.1%
Memory size1.3 MiB
2022-09-30T21:41:11.207748image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0844
Q10.311
median0.536
Q30.743
95-th percentile0.936
Maximum1
Range1
Interquartile range (IQR)0.432

Descriptive statistics

Standard deviation0.2644767979
Coefficient of variation (CV)0.504214137
Kurtosis-1.069433116
Mean0.5245326904
Median Absolute Deviation (MAD)0.216
Skewness-0.1007285168
Sum91472.73135
Variance0.06994797661
MonotocityNot monotonic
2022-09-30T21:41:11.324478image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.961720
 
0.4%
0.962599
 
0.3%
0.963516
 
0.3%
0.964460
 
0.3%
0.965401
 
0.2%
0.96380
 
0.2%
0.966366
 
0.2%
0.967332
 
0.2%
0.968273
 
0.2%
0.594254
 
0.1%
Other values (1697)170088
97.5%
ValueCountFrequency (%)
0173
0.1%
1 × 10540
 
< 0.1%
6.41 × 1051
 
< 0.1%
0.001731
 
< 0.1%
0.002131
 
< 0.1%
0.002281
 
< 0.1%
0.002981
 
< 0.1%
0.003111
 
< 0.1%
0.003921
 
< 0.1%
0.004371
 
< 0.1%
ValueCountFrequency (%)
14
< 0.1%
0.9971
 
< 0.1%
0.9961
 
< 0.1%
0.9952
 
< 0.1%
0.9943
< 0.1%
0.9932
 
< 0.1%
0.9921
 
< 0.1%
0.9913
< 0.1%
0.997
< 0.1%
0.9895
< 0.1%

year
Real number (ℝ≥0)

Distinct102
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1977.061764
Minimum1920
Maximum2021
Zeros0
Zeros (%)0.0%
Memory size1.3 MiB
2022-09-30T21:41:11.439477image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1920
5-th percentile1932
Q11955
median1977
Q31999
95-th percentile2018
Maximum2021
Range101
Interquartile range (IQR)44

Descriptive statistics

Standard deviation26.90795027
Coefficient of variation (CV)0.01361007064
Kurtosis-1.050776262
Mean1977.061764
Median Absolute Deviation (MAD)22
Skewness-0.08096586531
Sum344777824
Variance724.0377878
MonotocityNot monotonic
2022-09-30T21:41:11.572023image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20204294
 
2.5%
20182714
 
1.6%
20132622
 
1.5%
20162349
 
1.3%
20192329
 
1.3%
20152300
 
1.3%
20142252
 
1.3%
20172156
 
1.2%
19912121
 
1.2%
19992075
 
1.2%
Other values (92)149177
85.5%
ValueCountFrequency (%)
1920349
 
0.2%
1921156
 
0.1%
1922121
 
0.1%
1923185
 
0.1%
1924236
 
0.1%
1925279
 
0.2%
19261329
0.8%
1927750
0.4%
19281275
0.7%
1929951
0.5%
ValueCountFrequency (%)
20211840
1.1%
20204294
2.5%
20192329
1.3%
20182714
1.6%
20172156
1.2%
20162349
1.3%
20152300
1.3%
20142252
1.3%
20132622
1.5%
20121959
1.1%

Interactions

2022-09-30T21:40:39.724901image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:39.905343image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:40.061957image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:40.221941image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:40.381481image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:40.536050image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:40.687072image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:40.852155image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:41.006748image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:41.162984image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:41.319219image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:41.472566image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:41.627126image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:41.791672image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:41.952672image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:42.112671image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:42.282670image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:42.436317image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:42.594178image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:42.754331image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:43.019859image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:43.176610image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:43.329462image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:43.504042image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:43.659211image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:43.825211image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:43.985213image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:44.141212image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:44.293212image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:44.449211image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:44.612449image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:44.768035image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:44.925680image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:45.082634image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:45.237571image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:45.391177image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:45.543751image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:45.694981image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:45.856004image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:46.016333image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:46.175969image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:46.329936image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:46.485654image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:46.643331image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:46.801161image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:46.955380image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:47.108869image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:47.272396image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:47.424458image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:47.582053image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:47.737460image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:47.898547image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:48.051739image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:48.210047image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:48.370251image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:48.526791image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:48.683593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:48.838273image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:48.986935image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:49.156968image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:49.312559image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:49.595934image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:49.745622image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:49.898764image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:50.045763image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:50.199765image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:50.349683image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:50.501685image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:50.650201image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:50.802201image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:50.954102image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:51.105299image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:51.256668image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:51.408063image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:51.557838image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:51.727806image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:51.880747image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:52.037996image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:52.196635image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:52.349635image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:52.502636image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:52.653855image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:52.810242image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:52.966594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:53.117596image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:53.273292image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:53.428373image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:53.584825image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:53.740055image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:53.893428image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:54.046427image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:54.200078image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:54.352307image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:54.506508image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:54.660305image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:54.807244image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:54.972585image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:55.131858image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:55.289565image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:55.448707image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:55.614081image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:55.765756image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:55.925730image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:56.077310image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:56.237377image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:56.376934image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:56.545402image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:56.697471image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:56.850650image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:57.012988image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:57.168704image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:57.490966image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:57.646973image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:57.814801image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:57.965043image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:58.119305image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:58.266082image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:58.421932image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:58.576687image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:58.731347image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:58.883338image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:59.038412image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:59.192824image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:59.346014image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:59.499494image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:59.652707image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:59.803097image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:40:59.956217image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:41:00.106650image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:41:00.260982image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:41:00.416230image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:41:00.564232image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:41:00.740100image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:41:00.892474image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:41:01.043312image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:41:01.200130image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:41:01.349479image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:41:01.503566image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:41:01.661672image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:41:01.827367image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:41:01.987367image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:41:02.142104image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:41:02.302287image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:41:02.453283image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:41:02.612261image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:41:02.769357image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:41:02.920612image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:41:03.078555image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:41:03.243271image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:41:03.414696image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:41:03.586264image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:41:03.739510image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:41:03.900015image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:41:04.056171image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:41:04.220616image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-09-30T21:41:04.375616image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2022-09-30T21:41:11.685181image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-09-30T21:41:11.893120image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-09-30T21:41:12.115365image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-09-30T21:41:12.623044image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2022-09-30T21:41:12.793681image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2022-09-30T21:41:04.861799image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-09-30T21:41:05.326883image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

acousticnessartistsdanceabilityduration_msenergyexplicitidinstrumentalnesskeylivenessloudnessmodenamepopularityrelease_datespeechinesstempovalenceyear
00.991000['Mamie Smith']0.5981683330.224000cS0A1fUEUd1EW3FcF8AEI0.00052250.3790-12.6280Keep A Song In Your Soul1219200.0936149.9760.63401920
10.643000["Screamin' Jay Hawkins"]0.8521502000.517000hbkKFIJm7Z05H8Zl9w30f0.02640050.0809-7.2610I Put A Spell On You71920-01-050.053486.8890.95001920
20.993000['Mamie Smith']0.6471638270.1860011m7laMUgmOKqI3oYzuhne0.00001800.5190-12.0981Golfing Papa419200.174097.6000.68901920
30.000173['Oscar Velazquez']0.7304220870.7980019Lc5SfJJ5O1oaxY0fpwfh0.80100020.1280-7.3111True House Music - Xavier Santos & Carlos Gomix Remix171920-01-010.0425127.9970.04221920
40.295000['Mixe']0.7041652240.707012hJjbsLCytGsnAHfdsLejp0.000246100.4020-6.0360Xuniverxe21920-10-010.0768122.0760.29901920
50.996000['Mamie Smith & Her Jazz Hounds']0.4241986270.245003HnrHGLE9u2MjHtdobfWl90.79900050.2350-11.4701Crazy Blues - 78rpm Version919200.0397103.8700.47701920
60.992000['Mamie Smith']0.7821952000.057305DlCyqLyX2AOVDTjjkDZ8x0.00000250.1760-12.4531Don't You Advertise Your Man519200.059285.6520.48701920
70.996000['Mamie Smith & Her Jazz Hounds']0.4741861730.2390002FzJbHtqElixxCmrpSCUa0.18600090.1950-9.7121Arkansas Blues019200.028978.7840.36601920
80.996000['Francisco Canaro']0.4691468400.2380002i59gYdjlhBmbbWhf8YuK0.96000080.1490-18.7171La Chacarera - Remasterizado01920-07-080.0741130.0600.62101920
90.006820['Meetya']0.5714763040.7530006NUxS2XL3efRh0bloxkHm0.87300080.0920-6.9431Broken Puppet - Original Mix01920-01-010.0446126.9930.11901920

Last rows

acousticnessartistsdanceabilityduration_msenergyexplicitidinstrumentalnesskeylivenessloudnessmodenamepopularityrelease_datespeechinesstempovalenceyear
1743790.79500['Alessia Cara']0.4291447200.211045XnLMuqf3vRfskEAMUeCH0.00000040.196-11.6651A Little More02021-01-220.036094.7100.2282021
1743800.04840['Stephan F', 'YA-YA']0.6931771480.82601Cbf6PLWsL4s51eFepXx6L0.00001210.231-2.6691Only Tonight - Radio Edit02020-12-250.0762126.0490.3612020
1743810.79500['Alessia Cara']0.4291447200.21104pPFI9jsguIh3wC7Otoyy80.00000040.196-11.6651A Little More02021-01-220.036094.7100.2282021
1743820.14100['BigBankCarti', 'Keyvo400']0.5442150140.40713ASGdyWXeXsXtOIWtm0tv40.00000040.253-12.7450LayUp02020-12-310.2330129.7500.4902020
1743830.79500['Alessia Cara']0.4291447200.211052YtxLVUyvtiGPxwwxayHZ0.00000040.196-11.6651A Little More02021-01-220.036094.7100.2282021
1743840.00917['DJ Combo', 'Sander-7', 'Tony T']0.7921476150.866046LhBf6TvYjZU2SMvGZAbn0.00006060.178-5.0890The One02020-12-250.0356125.9720.1862020
1743850.79500['Alessia Cara']0.4291447200.21107tue2Wemjd0FZzRtDrQFZd0.00000040.196-11.6651A Little More02021-01-220.036094.7100.2282021
1743860.80600['Roger Fly']0.6712181470.589048Qj61hOdYmUCFJbpQ29Ob0.92000040.113-12.3930Together02020-12-090.0282108.0580.7142020
1743870.92000['Taylor Swift']0.4622440000.24011gcyHQpBQ1lfXGdhZmWrHP0.00000000.113-12.0771champagne problems692021-01-070.0377171.3190.3202021
1743880.23900['Roger Fly']0.6771977100.460057tgYkWQTNHVFEt6xDKKZj0.89100070.215-12.2371Improvisations02020-12-090.0258112.2080.7472020

Duplicate rows

Most frequent

acousticnessartistsdanceabilityduration_msenergyexplicitidinstrumentalnesskeylivenessloudnessmodenamepopularityrelease_datespeechinesstempovalenceyearcount
1510.000158['Year 200X']0.2571312530.720001xQvPFljQXA3GCK869ERvC0.82100070.1570-6.7041Ducktales (the Moon)2220080.039082.5130.42420089
9300.054400['Eagles']0.6572412270.538007tJS1cjSD1P8bodNGblYiK0.03430090.3320-10.3401Funky New Year - 2013 Remaster3719940.059190.8100.78519949
17350.969000['Schoolgirl Byebye']0.314743020.085500UsmyJDsst2xhX1ZiFF3JW0.79500090.1600-15.7751Year,2015182020-09-160.034269.8930.16120209
90.000003['Year 200X']0.1611140000.693007GlixhQpXo76vgALqoJ3L50.86300070.2320-8.5390Castlevania III (the Beginning)1820080.0512109.7840.48420088
130.000004['Year 200X']0.2781069730.788002pZDhsmGRSkRgWNfkDr70S0.96700070.0681-8.4391Ghosts N Goblins2020080.043492.9970.54220088
690.000047['Year 200X']0.1183862270.709004gpjBYc0lAkhYnmnfMeq4g0.88000020.0709-8.9031Zelda II (Title - Battle - Fairy - Palace)1920080.063173.7630.14120088
5550.004990['Armin van Buuren']0.397365000.638007tH6tGz6cQtpYReqHTlyjN0.00000020.3640-7.2561A State Of Trance (ASOT 996) - Tune Of The Year 2019 Top 3, Pt. 2302020-12-240.1760134.2840.67720208
7920.023600['Armin van Buuren']0.542338000.794007Kh32CyazzTdVEBXjKINVO0.00000070.1480-6.4131A State Of Trance (ASOT 996) - Tune Of The Year 2019 Top 3, Pt. 1302020-12-240.3030143.3770.81020208
590.000037['Third Eye Blind']0.4092009070.860004OtTVS1297d8h56MAX4Wpy0.00020440.0816-5.0361Losing a Whole Year - 2006 Remaster272006-07-180.0369175.6710.55420067
3100.000717['Suffused', 'MSZ']0.5854573060.866007FmPp4BjDVaEwhsEO7B7Oe0.84600090.1590-6.1260Year 2008 - MSZ Remix222020-01-060.0340127.0090.45020207